In many information technology organizations, workers, customers, and other users typically interact with multiple systems and applications during daily routines. Further, due to customers, partners, and employees increasingly demanding anytime, anywhere access to critical applications, information, and services, virtualized data centers, cloud applications, and other distributed work environments have become technologies with increasing importance in the information technology community. However, although recent information technology developments have created new opportunities to increase revenues, manage costs, and deepen relationships with users, diagnosing or otherwise supporting information technology data centers can present significant challenges. In particular, systems that currently attempt to diagnose or otherwise resolve problems associated with information technology systems tend to involve a sequence of diagnostic steps commonly suggested to provide potential options to fix or diagnose a problem. For example, a support representative may ask a customer to change a certain configuration parameter, restart a service, reboot a machine, and so on throughout the diagnostic process. However, these steps often must be done one at a time, and moreover, often have the potential to impact functions in production systems that may be in active use.
Consequently, managing diagnostic and other troubleshooting processes presents an ongoing challenge in the information technology community. Oftentimes, existing approaches to resolve user troubleshooting requests can break down due to information silos that present barriers to sharing knowledge that may address a particular problem. In modern information technology environments, which are increasingly complex, troubleshooting problematic systems requires visibility into the infrastructure to successfully support services that may be running therein. However, techniques currently used to diagnose problematic systems typically involve sequential and time-consuming trial-and-error approaches, which even if successful, are typically not modeled in a manner that would enable subsequent diagnostic processes to utilize any knowledge gained from the prior diagnosis effort. Moreover, because existing diagnostic techniques typically take control over a system that has been reported problematic, productivity often suffers while the diagnostic processes occupy needed resources.